PhD: Optimal WDS management during critical scenarios

Code	i3WaterSDC12
Host Institution	UNIVERSITA DEGLI STUDI DI NAPOLI FEDERICO II Department of Civil, Architectural and Environmental Engineering, Address: via Claudio 21, Naples
Location	ITALY, NAPOLI Postcode 80125
Supervisor(s)	Main Supervisor: Dr Cristiana Di Cristo (UNINA, Italy) Co-supervisor: Dr Claudia Quintiliani (BW, The Netherlands) Industrial Mentor – Experts: Dr. Tony Esposito (GORI, Italy)
Research Field	Artificial Intelligence
Contract type	Fixed term contract
Application Deadline	september 30th, 2026

Description

Research Objectives

The main research goal of iWaterS is to provide, for the first time, rational analyses and explainable intelligent decision support of WDS to increase resilience to day-to-day incidents and to extreme events in the context of climate change such as floods or droughts, through new interdisciplinary and integral approaches for exploiting datasets (on-line, off-line), intelligent models (data-driven, numerical) and simulation results (digital twins, multiagent systems).

The specific research objective of this call is to develop intelligent methodologies for the analysis, management, and optimization of Water Distribution Systems (WDSs) under critical operating conditions, including infrastructure failures, climate change impacts, and accidental or intentional contamination events, with the aim of ensuring the continuity, reliability, and safety of drinking water services in terms of both water quantity and quality while enhancing the security and resilience of WDSs..

The candidate will contribute to achieving this objective through the following sub-objectives:

sub-Objectives: 1) Identify and characterize future critical scenarios affecting Water Distribution Systems (WDSs), including infrastructure failures, variations in water demand and the number of users, climate change impacts, and accidental or intentional contamination events, through data collection and analysis, and predict system responses using numerical simulations and/or complex network approaches. Given the high level of uncertainty associated with input data and future scenarios, both deterministic and stochastic modelling techniques will be employed. 2) Develop and apply optimization models to evaluate and compare management strategies under the identified scenarios, assessing the reliability, resilience, and adaptive capacity of WDSs. The outcomes will support the development of innovative methodologies and practical decision-support tools for improving resource management, operational efficiency, and the security of drinking water distribution systems.

Expected Results: 1) Support water utilities in enhancing the safety, reliability, and resilience of WDSs, ensuring the continuous provision of high-quality drinking water to consumers. 2) Produce innovative tools and management strategies for the efficient operation of WDSs under critical scenarios, preventing service disruptions, mitigating water quality deterioration, and improving the systems’ capacity to respond and adapt to infrastructure failures, climate change impacts, and contamination events.